restore hidden visibility by default for Linux builds (#20461)

Summary:
Symbols are given hidden visibility by default on Linux to emulate the behavior on Windows.  This helps developers catch visibility issues in their streamlined Linux dev environment before being surprised, late in the process, by Windows errors.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/20461

Reviewed By: kostmo

Differential Revision: D15410410

Pulled By: dzhulgakov

fbshipit-source-id: 1d684b5a9a80b692966a775c3f1c56b7c72ffc95

.jenkins/pytorch/build-asan.sh

@@ -29,11 +29,11 @@ export ASAN_OPTIONS=detect_leaks=0:symbolize=1
 # [2] https://wiki.gentoo.org/wiki/AddressSanitizer/Problems
 # [3] https://github.com/Kitware/CMake/commit/e9a1ddc594de6e6251bf06d732775dae2cabe4c8
 #
-# TODO: Make the ASAN flags a more unified env var
+# TODO: Make the ASAN flags a centralized env var and unify with USE_ASAN option
 CC="clang" CXX="clang++" LDSHARED="clang --shared" \
   CFLAGS="-fsanitize=address -fsanitize=undefined -fno-sanitize-recover=all -shared-libasan -pthread" \
   CXX_FLAGS="-pthread" \
-  NO_CUDA=1 USE_MKLDNN=0 \
+  USE_ASAN=1 NO_CUDA=1 USE_MKLDNN=0 \
   python setup.py install
 
 assert_git_not_dirty

caffe2/CMakeLists.txt

@@ -818,9 +818,15 @@ if(USE_ROCM)
 endif()
 
 
-if (NOT WIN32)
-    # FIXME kostmo
-#  target_compile_options(caffe2 PRIVATE "-fvisibility=hidden")
+if (NOT WIN32 AND NOT USE_ASAN)
+  # Enable hidden visibility by default to make it easier to debug issues with
+  # TORCH_API annotations. Hidden visibility with selective default visibility
+  # behaves close enough to Windows' dllimport/dllexport.
+  #
+  # Unfortunately, hidden visibility messes up some ubsan warnings because
+  # templated classes crossing library boundary get duplicated (but identical)
+  # definitions. It's easier to just disable it.
+  target_compile_options(caffe2 PRIVATE "-fvisibility=hidden")
 endif()
 
 

test/cpp/jit/test.cpp

@@ -31,6 +31,7 @@
 #include <test/cpp/jit/test_qualified_name.h>
 #include <test/cpp/jit/test_subgraph_matcher.h>
 #include <test/cpp/jit/test_subgraph_utils.h>
+#include <torch/csrc/WindowsTorchApiMacro.h>
 
 using namespace torch::jit::script;
 using namespace torch::jit::test;
@@ -110,7 +111,7 @@ TH_FORALL_TESTS_CUDA(JIT_GTEST_CUDA)
 #endif
 
 #define JIT_TEST(name) test##name();
-void runJITCPPTests(bool runCuda) {
+TORCH_API void runJITCPPTests(bool runCuda) {
   TH_FORALL_TESTS(JIT_TEST)
   if (runCuda) {
     TH_FORALL_TESTS_CUDA(JIT_TEST)

tools/build_pytorch_libs.py

@@ -211,6 +211,7 @@ def run_cmake(version,
         USE_REDIS=os.getenv('USE_REDIS'),
         USE_GLOG=os.getenv('USE_GLOG'),
         USE_GFLAGS=os.getenv('USE_GFLAGS'),
+        USE_ASAN=check_env_flag('USE_ASAN'),
         WERROR=os.getenv('WERROR'))
 
     if os.getenv('_GLIBCXX_USE_CXX11_ABI'):

torch/csrc/api/include/torch/nn/modules/conv.h

@@ -78,7 +78,7 @@ struct ConvOptions {
 
 /// Base class for all (dimension-specialized) convolution modules.
 template <size_t D, typename Derived>
-class ConvImpl : public torch::nn::Cloneable<Derived> {
+class TORCH_API ConvImpl : public torch::nn::Cloneable<Derived> {
  public:
   ConvImpl(
       int64_t input_channels,

torch/csrc/api/include/torch/nn/modules/dropout.h

@@ -40,7 +40,7 @@ class DropoutImplBase : public torch::nn::Cloneable<Derived> {
 /// about the exact semantics of this module.
 class TORCH_API DropoutImpl : public detail::DropoutImplBase<DropoutImpl> {
  public:
-  using detail::DropoutImplBase<DropoutImpl>::DropoutImplBase;
+  explicit DropoutImpl(DropoutOptions options_ = DropoutOptions());
 
   /// During training, applies a noise mask to the input tensor.
   /// During evaluation, applies an identity function.
@@ -62,7 +62,7 @@ class TORCH_API DropoutImpl : public detail::DropoutImplBase<DropoutImpl> {
 class TORCH_API FeatureDropoutImpl
     : public detail::DropoutImplBase<FeatureDropoutImpl> {
  public:
-  using detail::DropoutImplBase<FeatureDropoutImpl>::DropoutImplBase;
+  explicit FeatureDropoutImpl(DropoutOptions options_ = DropoutOptions());
 
   /// During training, applies a noise mask to the input tensor.
   /// During evaluation, applies an identity function.

torch/csrc/api/include/torch/nn/modules/rnn.h

@@ -53,7 +53,7 @@ struct TORCH_API RNNOptionsBase {
 
 /// Base class for all RNN implementations (intended for code sharing).
 template <typename Derived>
-class RNNImplBase : public torch::nn::Cloneable<Derived> {
+class TORCH_API RNNImplBase : public torch::nn::Cloneable<Derived> {
  public:
   /// These must line up with the CUDNN mode codes:
   /// https://docs.nvidia.com/deeplearning/sdk/cudnn-developer-guide/index.html#cudnnRNNMode_t

torch/csrc/api/src/nn/modules/dropout.cpp

@@ -27,6 +27,8 @@ template class DropoutImplBase<FeatureDropoutImpl>;
 
 DropoutOptions::DropoutOptions(double rate) : rate_(rate) {}
 
+DropoutImpl::DropoutImpl(DropoutOptions options_) : DropoutImplBase(options_) {}
+
 Tensor DropoutImpl::forward(const Tensor& input) {
   return torch::dropout(input, options.rate_, this->is_training());
 }
@@ -35,6 +37,9 @@ void DropoutImpl::pretty_print(std::ostream& stream) const {
   stream << "torch::nn::Dropout(rate=" << options.rate_ << ")";
 }
 
+FeatureDropoutImpl::FeatureDropoutImpl(DropoutOptions options_)
+    : DropoutImplBase(options_) {}
+
 Tensor FeatureDropoutImpl::forward(const Tensor& input) {
   return torch::feature_dropout(input, options.rate_, this->is_training());
 }

torch/csrc/autograd/profiler.cpp

@@ -27,6 +27,8 @@ std::list<std::shared_ptr<RangeEventList>> all_event_lists;
 thread_local std::shared_ptr<RangeEventList> event_list;
 thread_local uint16_t thread_id;
 
+ProfilerConfig::~ProfilerConfig() = default;
+
 RangeEventList& getEventList() {
   if (!event_list) {
     std::lock_guard<std::mutex> guard(all_event_lists_mutex);

torch/csrc/autograd/profiler.h

@@ -101,9 +101,10 @@ enum class TORCH_API ProfilerState {
     NVTX,  // only emit NVTX markers
 };
 
-struct ProfilerConfig {
+struct TORCH_API ProfilerConfig {
   ProfilerConfig(ProfilerState state, bool report_input_shapes)
       : state(state), report_input_shapes(report_input_shapes) {}
+  ~ProfilerConfig();
   ProfilerState state;
   bool report_input_shapes;
 };

torch/csrc/jit/passes/requires_grad_analysis.cpp

@@ -2,6 +2,7 @@
 #include <torch/csrc/jit/constants.h>
 #include <torch/csrc/jit/ir.h>
 #include <torch/csrc/jit/operator.h>
+#include <torch/csrc/jit/passes/requires_grad_analysis.h>
 
 #include <vector>
 

torch/csrc/jit/passes/utils/memory_dag.h

@@ -29,23 +29,30 @@ struct Value;
 //
 // So, by traversing the "points-to" graph to the leaves, you can determine
 // which memory locations an element may point to.
-class MemoryDAG {
+class TORCH_API MemoryDAG {
  public:
+
+  // explicitly delete copy constructor because otherwise windows build is confused for an exported class
+  // see https://stackoverflow.com/a/51033485/105137
+  MemoryDAG() {}
+  MemoryDAG(const MemoryDAG&)=delete;
+  MemoryDAG& operator=(const MemoryDAG&)=delete;
+
   // Make `from` point at `to`.
-  TORCH_API void makePointerTo(Element* from, Element* to);
+  void makePointerTo(Element* from, Element* to);
 
   void addToContainedElements(Element* contained, Element* container);
 
   // Make a fresh element (i.e. an element that doesn't point to anything) and
   // return it.
-  TORCH_API Element* makeFreshValue(const Value* v);
+  Element* makeFreshValue(const Value* v);
 
   // Do `a` and `b` potentially share a memory location?
   bool mayAlias(const Element* a, const Element* b) const;
-  TORCH_API bool mayAlias(Element* a, Element* b) const;
+  bool mayAlias(Element* a, Element* b) const;
 
   // Does a hold reference to any memory that is stored in elem, or vice versa?
-  TORCH_API bool mayContainAlias(const Element* a, const Element* b) const;
+  bool mayContainAlias(const Element* a, const Element* b) const;
   bool mayContainAlias(Element* a, Element* b) const;
 
   bool mayContainAlias(

ubsan.supp

@@ -1,2 +1,3 @@
 vptr:libtorch.so
+vptr:libcaffe2.so
 bounds:asmjit::Zone::_alloc